ABSTRACT

Heats of explosion of 69 double base propellants and 62 composite modified double base (CMDB) propellants with different compositions were measured experimentally. These data and the measured values from the other references were used for the evaluation of heats of explosion of different types of energetic materials. Artificial neural network (ANN) and multiple linear regression (MLR) models were developed for this purpose. Two series of data containing 90 and 78 data were applied for modeling of double base and CMDB propellants, respectively. Each series was separated randomly into two groups, training and prediction sets, respectively, which were used for generation and evaluation of suitable models. The predicted results of ANN and MLR models were more reliable than those obtained by mass percentages and heats of explosion of individual components.